WHAT IS data science?
Data science is a technique that is supposed to increase the performance of an organization or a company. With the help of this technique, different statistical methods and models are made which are implemented on the projects, products, and data of an organization. data science is a data-driven approach. This means this technique is based on the data. At present, the companies and organizations are switching to data-driven approaches from traditional methods. Data science is also beneficial in recognizing the hidden problems of the strategy because of how the performance of the company falls. know more about the data science course.
The data science majorly depends on two things which are listed below:
- good quality of the data
- skilled data scientists
TYPES OF data science
Here are some types of data science listed below:
- DESCRIPTIVE ANALYTICS
In descriptive analytics, the data of the past years are analyzed. Along with the data, problems that have occurred in the past few years are also analyzed. The goal of descriptive analytics is to find out the reasons or causes of these problems. Why did these problems occur?
- PREDICTIVE ANALYTICS
In predictive analytics, the past, as well as the present data, is analyzed. The problems or conditions of the present time are analyzed and based on this analysis, future conditions are predicted.
- PRESCRIPTIVE ANALYTICS
In the prescriptive analysis, the problems of the present time are analyzed. The goal of the prescriptive analysis is to solve the problems of the present time.
WORKING OF THE data science
It is clearly understood what a company or organization wants to achieve in the end. According to that analytical method, the required data is collected from different resources. Along with the collection of the data, cleansing of the data and integration of the data is also done. Initially, the operations are performed on a small set of data or a sample of the data. Analytical tools offer many services to the user. Some of them are storing the data into the spreadsheets, performing statistical analysis on it, predictive modeling, data mining, etc.
The analytical process is iterative. The process is repeated if required. This is because when new and hidden patterns are recognized from the data, then the analysis is done again on that new data.
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